The detection of natural cities in the Netherlands—Nocturnal satellite imagery and Zipf’s law

Bergs, Rolf

doi:10.1007/s10037-018-0122-6

Cited by 13 publications

(6 citation statements)

References 36 publications

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“…In order to correctly characterize urban shrinkage and expansion, this paper redefines a city to reflect its real and natural central area. In other words, a natural city is a natural and objective description of urban scope according to the density of human settlements and activities [90,91].…”

Section: Redefining Natural Citiesmentioning

confidence: 99%

Research on Large-Scale Urban Shrinkage and Expansion in the Yellow River Affected Area Using Night Light Data

Niu

Xia

Wang

et al. 2020

IJGI

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As the land use issue, caused by urban shrinkage in China, is becoming more and more prominent, research on urban shrinkage and expansion has become particularly challenging and urgent. Based on the points of interest (POI) data, this paper redefines the scope, quantity, and area of natural cities by using threshold methods, which accurately identify the shrinkage and expansion of cities in the Yellow River affected area using night light data in 2013 and 2018. The results show that: (1) there are 3130 natural cities (48,118.75 km2) in the Yellow River affected area, including 604 shrinking cities (8407.50 km2) and 2165 expanding cities (32,972.75 km2). (2) The spatial distributions of shrinking and expanding cities are quite different. The shrinking cities are mainly located in the upper Yellow River affected area, except for the administrative cities of Lanzhou and Yinchuan; the expanding cities are mainly distributed in the middle and lower Yellow River affected area, and the administrative cities of Lanzhou and Yinchuan. (3) Shrinking and expanding cities are typically smaller cities. The research results provide a quick data supported approach for regional urban planning and land use management, for when regional and central governments formulate the outlines of urban development monitoring and regional planning.

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Section: Redefining Natural Citiesmentioning

confidence: 99%

Research on Large-Scale Urban Shrinkage and Expansion in the Yellow River Affected Area Using Night Light Data

Niu

Xia

Wang

et al. 2020

IJGI

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“…In the formula, TPi and TRi represent the total population size and rank of the i-th city, TP1 represents the total population size of the first city (city 1), and q1 represents the elastic coefficient (constant). Empirical studies in many countries have proven that the urban population size is subject to Zipf's law [70,71], including America [72][73][74], China [75][76][77], Canada [78], Germany [79], Denmark [80], Croatia [81], Netherlands [82], Polish [83], Malaysia [84], India [85], South Africa [86], and Turkey [87].…”

Section: Methodsmentioning

confidence: 99%

Study on Fractal Characteristics of Migration-Population Flow—Evidence from Egypt

Zhao

Wang

2021

IJGI

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Population migration is a major event of optimizing the allocation of production factors and a key way to construct regional relations and promote spatial reconstruction. However, there are few papers published on population migration that have a direct impact on the sustainable development of deserts owing to the more sensitive and complex man–earth relationship. Therefore, it is important to study the laws and characteristics of population migration in such regions. The study of Egypt by Zipf’s law shows that the spatial distribution of migration population size conforms to the law of power function with fractal characteristics of different types. The migration population is generally in a “Pareto” state in spatial distribution. Decentralization power is the leading driving force of spatial distribution, and scale-free regional distribution shows significant spatial agglomeration and gradient. Limited by research scope and “heavy tail” factors, Zipf’s law is not completely applicable. The spatial pattern and flow field characteristics of the migration population are analyzed in this paper by the conversion from “structural fractal” to “spatial fractal” based on scale-free geographic projection, providing the reference for the formulation of population governance policies and spatial planning strategies in Egypt and more desert countries.

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“…Unsupervised classification comprises traditional cluster analysis like k‑means or Isodata, e.g., to segment urban and non-urban space based on population density data or night satellite images, classify land use, or detect pollution-affected regions (e.g., Small et al. 2006 ; Bergs 2018 ). Furthermore, CLARA (Clustering for Large Applications) and kernel density estimation are methods to classify space (e.g., Budde 2018 ; Budde and Neumann 2019 ).…”

Section: The Different Dimensions Of the Use Of Small-scale Spatial Datamentioning

confidence: 99%

The potential of small-scale spatial data in regional science

Bergs¹,

Budde

2022

Rev Reg Res

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The detection of natural cities in the Netherlands—Nocturnal satellite imagery and Zipf’s law

Cited by 13 publications

References 36 publications

Research on Large-Scale Urban Shrinkage and Expansion in the Yellow River Affected Area Using Night Light Data

Research on Large-Scale Urban Shrinkage and Expansion in the Yellow River Affected Area Using Night Light Data

Study on Fractal Characteristics of Migration-Population Flow—Evidence from Egypt

The potential of small-scale spatial data in regional science

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